相信许多留学生对数学代考都不陌生,国外许多大学都引进了网课的学习模式。网课学业有利有弊,学生不需要到固定的教室学习,只需要登录相应的网站研讨线上课程即可。但也正是其便利性,线上课程的数量往往比正常课程多得多。留学生课业深重,时刻名贵,既要学习知识,又要结束多种类型的课堂作业,physics作业代写,物理代写,论文写作等;网课考试很大程度增加了他们的负担。所以,您要是有这方面的困扰,不要犹疑,订购myassignments-help代考渠道的数学代考服务,价格合理,给你前所未有的学习体会。

我们的数学代考服务适用于那些对课程结束没有掌握,或许没有满足的时刻结束网课的同学。高度匹配专业科目,按需结束您的网课考试、数学代写需求。担保买卖支持,100%退款保证,免费赠送Turnitin检测报告。myassignments-help的Math作业代写服务,是你留学路上忠实可靠的小帮手!


经济代写|计量经济学代写Econometrics代考|The Outer-Product-of-the-Gradient Regression

We remarked in the introduction to this chapter that the Gauss-Newton regression is not generally applicable to models estimated by maximum likelihood. In view of the extreme usefulness of the GNR for computing test statistics in the context of nonlinear regression models, it is of much interest to see if other artificial regressions with similar properties are available in the context of models estimated by maximum likelihood.

One preliminary and obvious remark: No regression, artificial or otherwise, is needed to implement the LR test. Since any package capable of producing ML estimates will certainly also produce the maximized loglikelihood function, there can be no obstacle to performing an LR test unless there is some difficulty in estimating either the restricted or the unrestricted model. In many cases, there is no such difficulty, and then the LR test is almost always the procedure of choice. However, there are occasions when one of the two models is much easier to estimate than the other, and then one would wish to use either the LM or the Wald test to avoid the more difficult estimation. Another possibility is that the alternative hypothesis may be implicit rather than being associated with a well-defined parametrized model that includes the null hypothesis as a special case. We have seen in the context of the GNR that many diagnostic tests fall into this category. When the alternative hypothesis is implicit, one would almost always wish to use an LM test.

In the regression context, the GNR provides a means of computing test statistics based on the LM principle. In point of fact, as we saw in Section 6.7, it can be used to compute test statistics based on any root- $n$ consistent estimates. We will now introduce a new artificial regression, called the outerproduct-of-the-gradient regression, or the OPG regression for short, which can be used with any model estimated by maximum likelihood. The OPG regression was first used as a means of computing test statistics by Godfrey and Wickens (1981). This artificial regression, which is very easy indeed to set up for most models estimated by maximum likelihood, can be used for the same purposes as the GNR: verification of first-order conditions for the maximization of the loglikelihood function, covariance matrix estimation, one-step efficient estimation, and, of greatest immediate interest, the computation of test statistics.

经济代写|计量经济学代写Econometrics代考|Further Reading and Conclusion

The three classical tests, as the word “classical” implies, have a long history and have generated a great deal of literature; see Engle (1984) and Godfrey (1988) for references. In this chapter, we have tried to emphasize the common aspects of tests underlying the very considerable diversity of testing procedures and to emphasize the geometrical interpretation of the tests. A simpler discussion of the geometry of the classical tests may be found in Buse (1982). We have pointed out that there is a common asymptotic random variable to which all the classical test statistics tend as the sample size tends to infinity and that the distribution of this asymptotic random variable is chi-squared, central if the null hypothesis under test is true, and noncentral otherwise. The actual noncentrality parameter is a function of the drifting DGP considered as a model of the various possibilities that exist in the neighborhood of the null hypothesis. Because the mathematics involved is not elementary, we did not discuss the details of how this noncentrality parameter may be derived, but the intuition is essentially the same as for the case of nonlinear regression models discussed in Section 12.4.

The asymptotic properties of the classical tests under DGPs other than those satisfying the null hypothesis is studied in a well-known article of Gallant and Holly (1980) as well as in the survey article of Engle (1984). In these articles, only drifting DGPs that satisfied the alternative hypothesis were taken into account. The Gallant and Holly article provoked a substantial amount of further research. One landmark of the literature in which this research is reported is a paper by Burguete, Gallant, and Souza (1982), in which an ambitious project of unification of a wide variety of asymptotic methods is undertaken. Here, for the first time, drifting DGPs were considered which, although in the neighborhood of the null hypothesis, satisfied neither the null nor the alternative hypothesis. Subsequently, Newey (1985a) and Tauchen (1985) continued the investigation of this approach and were led to propose new tests and still more testing procedures (see Chapter 16). Our own paper (Davidson and MacKinnon, 1987) pursued the study of general local DGPs and was among the first to try to set the theory of hypothesis testing in a geometrical framework in such a way that “neighborhoods” of a null hypothesis could be formally defined and mentally visualized. The geometrical approach had been gaining favor with econometricians and, more particularly, statisticians for some time before this and had led to the syntheses found in Amari (1985) and Barndorff-Nielsen, Cox, and Reid (1986); see the survey article by Kass (1989). We should warn readers, however, that the last few references cited use mathematics that is far from elementary.

经济代写|计量经济学代写Econometrics代考|ECON2271

计量经济学代考

经济代写|计量经济学代写Econometrics代考|The Outer-Product-of-the-Gradient Regression

我们在本章的介绍中指出,高斯-牛顿回归通常不适用于通过最大似然估计的模型。鉴于 GNR 在非线性回归模型的背景下计算测试统计数据的极端有用性,因此在通过最大似然估计的模型的背景下是否可以使用具有类似属性的其他人工回归是非常有趣的。

一个初步且明显的评论:实施 LR 测试不需要人为或其他方式的回归。由于任何能够产生 ML 估计的包肯定也会产生最大化的对数似然函数,因此执行 LR 测试不会有任何障碍,除非在估计受限模型或非受限模型时存在一些困难。在许多情况下,没有这样的困难,然后 LR 测试几乎总是选择的程序。然而,有时这两种模型中的一种比另一种更容易估计,然后人们希望使用 LM 或 Wald 检验来避免更困难的估计。另一种可能性是备择假设可能是隐含的,而不是与包含零假设作为特例的定义明确的参数化模型相关联。我们在 GNR 的背景下看到,许多诊断测试都属于这一类。当备择假设是隐含的时,人们几乎总是希望使用 LM 检验。

在回归上下文中,GNR 提供了一种基于 LM 原理计算测试统计数据的方法。事实上,正如我们在 6.7 节中看到的,它可以用来计算基于任何根的测试统计量——n一致的估计。我们现在将介绍一种新的人工回归,称为梯度外积回归,或简称 OPG 回归,它可以与任何通过最大似然估计的模型一起使用。OPG 回归首先被 Godfrey 和 Wickens (1981) 用作计算检验统计量的一种方法。这种人工回归确实很容易为大多数通过最大似然估计的模型设置,可以用于与 GNR 相同的目的:验证一阶条件以最大化对数似然函数、协方差矩阵估计、一个- 步骤有效的估计,以及最直接感兴趣的测试统计的计算。

经济代写|计量经济学代写Econometrics代考|Further Reading and Conclusion

三考,顾名思义,源远流长,产生了大量文学作品;参考 Engle (1984) 和 Godfrey (1988)。在本章中,我们试图强调测试程序非常多样化的测试的共同方面,并强调测试的几何解释。可以在 Buse (1982) 中找到对经典测试几何的更简单讨论。我们已经指出,当样本量趋于无穷大时,所有经典检验统计量都趋向于一个共同的渐近随机变量,并且这个渐近随机变量的分布是卡方的,如果所测试的原假设为真,则处于中心位置,否则是非中心的。实际的非中心性参数是漂移 DGP 的函数,被认为是存在于零假设附近的各种可能性的模型。因为所涉及的数学不是初等的,所以我们没有讨论如何导出这个非中心性参数的细节,但直觉与 12.4 节讨论的非线性回归模型的情况基本相同。

在 Gallant 和 Holly (1980) 的著名文章以及 Engle (1984) 的调查文章中研究了 DGP 下经典检验的渐近特性,而不是满足零假设的那些。在这些文章中,仅考虑了满足替代假设的漂移 DGP。Gallant 和 Holly 的文章引发了大量的进一步研究。报道这项研究的文献的一个里程碑是 Burguete、Gallant 和 Souza (1982) 的一篇论文,其中进行了一个雄心勃勃的项目,即统一各种渐近方法。在这里,第一次考虑了漂移 DGP,尽管它在原假设附近,但既不满足原假设也不满足替代假设。随后,Newey (1985a) 和 Tauchen (1985) 继续研究这种方法,并被引导提出新的测试和更多的测试程序(见第 16 章)。我们自己的论文 (Davidson and MacKinnon, 1987) 研究了一般的局部 DGP,并且是第一个尝试在几何框架中设置假设检验理论的论文之一,使得原假设的“邻域”可以正式定义和心理可视化。在此之前的一段时间内,几何方法一直受到计量经济学家,尤其是统计学家的青睐,并导致了在 Amari (1985) 和 Barndorff-Nielsen、Cox 和 Reid (1986) 中发现的综合;参见 Kass (1989) 的调查文章。然而,我们应该警告读者,最后引用的几篇参考文献使用的数学远非初级。

经济代写|博弈论代写Game Theory代考

myassignments-help数学代考价格说明

1、客户需提供物理代考的网址,相关账户,以及课程名称,Textbook等相关资料~客服会根据作业数量和持续时间给您定价~使收费透明,让您清楚的知道您的钱花在什么地方。

2、数学代写一般每篇报价约为600—1000rmb,费用根据持续时间、周作业量、成绩要求有所浮动(持续时间越长约便宜、周作业量越多约贵、成绩要求越高越贵),报价后价格觉得合适,可以先付一周的款,我们帮你试做,满意后再继续,遇到Fail全额退款。

3、myassignments-help公司所有MATH作业代写服务支持付半款,全款,周付款,周付款一方面方便大家查阅自己的分数,一方面也方便大家资金周转,注意:每周固定周一时先预付下周的定金,不付定金不予继续做。物理代写一次性付清打9.5折。

Math作业代写、数学代写常见问题

留学生代写覆盖学科?

代写学科覆盖Math数学,经济代写,金融,计算机,生物信息,统计Statistics,Financial Engineering,Mathematical Finance,Quantitative Finance,Management Information Systems,Business Analytics,Data Science等。代写编程语言包括Python代写、Physics作业代写、物理代写、R语言代写、R代写、Matlab代写、C++代做、Java代做等。

数学作业代写会暴露客户的私密信息吗?

我们myassignments-help为了客户的信息泄露,采用的软件都是专业的防追踪的软件,保证安全隐私,绝对保密。您在我们平台订购的任何网课服务以及相关收费标准,都是公开透明,不存在任何针对性收费及差异化服务,我们随时欢迎选购的留学生朋友监督我们的服务,提出Math作业代写、数学代写修改建议。我们保障每一位客户的隐私安全。

留学生代写提供什么服务?

我们提供英语国家如美国、加拿大、英国、澳洲、新西兰、新加坡等华人留学生论文作业代写、物理代写、essay润色精修、课业辅导及网课代修代写、Quiz,Exam协助、期刊论文发表等学术服务,myassignments-help拥有的专业Math作业代写写手皆是精英学识修为精湛;实战经验丰富的学哥学姐!为你解决一切学术烦恼!

物理代考靠谱吗?

靠谱的数学代考听起来简单,但实际上不好甄别。我们能做到的靠谱,是把客户的网课当成自己的网课;把客户的作业当成自己的作业;并将这样的理念传达到全职写手和freelancer的日常培养中,坚决辞退糊弄、不守时、抄袭的写手!这就是我们要做的靠谱!

数学代考下单流程

提早与客服交流,处理你心中的顾虑。操作下单,上传你的数学代考/论文代写要求。专家结束论文,准时交给,在此过程中可与专家随时交流。后续互动批改

付款操作:我们数学代考服务正常多种支付方法,包含paypal,visa,mastercard,支付宝,union pay。下单后与专家直接互动。

售后服务:论文结束后保证完美经过turnitin查看,在线客服全天候在线为您服务。如果你觉得有需求批改的当地能够免费批改,直至您对论文满意为止。如果上交给教师后有需求批改的当地,只需求告诉您的批改要求或教师的comments,专家会据此批改。

保密服务:不需求提供真实的数学代考名字和电话号码,请提供其他牢靠的联系方法。我们有自己的工作准则,不会泄露您的个人信息。

myassignments-help擅长领域包含但不是全部:

myassignments-help服务请添加我们官网的客服或者微信/QQ,我们的服务覆盖:Assignment代写、Business商科代写、CS代考、Economics经济学代写、Essay代写、Finance金融代写、Math数学代写、report代写、R语言代考、Statistics统计学代写、物理代考、作业代写、加拿大代考、加拿大统计代写、北美代写、北美作业代写、北美统计代考、商科Essay代写、商科代考、数学代考、数学代写、数学作业代写、physics作业代写、物理代写、数据分析代写、新西兰代写、澳洲Essay代写、澳洲代写、澳洲作业代写、澳洲统计代写、澳洲金融代写、留学生课业指导、经济代写、统计代写、统计作业代写、美国Essay代写、美国代考、美国数学代写、美国统计代写、英国Essay代写、英国代考、英国作业代写、英国数学代写、英国统计代写、英国金融代写、论文代写、金融代考、金融作业代写。